| --- |
| license: apache-2.0 |
| tags: |
| - Spatial-Temporal |
| - Graph |
| - Logistic |
| size_categories: |
| - 10M<n<100M |
| dataset_info: |
| features: |
| - name: order_id |
| dtype: int64 |
| - name: region_id |
| dtype: int64 |
| - name: city |
| dtype: string |
| - name: courier_id |
| dtype: int64 |
| - name: accept_time |
| dtype: string |
| - name: time_window_start |
| dtype: string |
| - name: time_window_end |
| dtype: string |
| - name: lng |
| dtype: float64 |
| - name: lat |
| dtype: float64 |
| - name: aoi_id |
| dtype: int64 |
| - name: aoi_type |
| dtype: int64 |
| - name: pickup_time |
| dtype: string |
| - name: pickup_gps_time |
| dtype: string |
| - name: pickup_gps_lng |
| dtype: float64 |
| - name: pickup_gps_lat |
| dtype: float64 |
| - name: accept_gps_time |
| dtype: string |
| - name: accept_gps_lng |
| dtype: float64 |
| - name: accept_gps_lat |
| dtype: float64 |
| - name: ds |
| dtype: int64 |
| splits: |
| - name: pickup_jl |
| num_bytes: 54225579 |
| num_examples: 261801 |
| - name: pickup_cq |
| num_bytes: 243174931 |
| num_examples: 1172703 |
| - name: pickup_yt |
| num_bytes: 237146694 |
| num_examples: 1146781 |
| - name: pickup_sh |
| num_bytes: 293399390 |
| num_examples: 1424406 |
| - name: pickup_hz |
| num_bytes: 436103754 |
| num_examples: 2130456 |
| download_size: 443251368 |
| dataset_size: 1264050348 |
| --- |
| # 1. About Dataset |
| **LaDe** is a publicly available last-mile delivery dataset with millions of packages from industry. |
| It has three unique characteristics: (1) Large-scale. It involves 10,677k packages of 21k couriers over 6 months of real-world operation. |
| (2) Comprehensive information, it offers original package information, such as its location and time requirements, as well as task-event information, which records when and where the courier is while events such as task-accept and task-finish events happen. |
| (3) Diversity: the dataset includes data from various scenarios, such as package pick-up and delivery, and from multiple cities, each with its unique spatio-temporal patterns due to their distinct characteristics such as populations. |
|
|
| If you use this dataset for your research, please cite this paper: {xxx} |
|
|
| # 2. Download |
| [LaDe](https://huggingface.co/datasets/Cainiao-AI/LaDe) is composed of two subdatasets: i) [LaDe-D](https://huggingface.co/datasets/Cainiao-AI/LaDe-D), which comes from the package delivery scenario. |
| ii) [LaDe-P](https://huggingface.co/datasets/Cainiao-AI/LaDe-P), which comes from the package pickup scenario. To facilitate the utilization of the dataset, each sub-dataset is presented in CSV format. |
|
|
| LaDe-P is the second subdataset from [LaDe](https://huggingface.co/datasets/Cainiao-AI/LaDe) |
| LaDe can be used for research purposes. Before you download the dataset, please read these terms. And [Code link](https://github.com/wenhaomin/LaDe). Then put the data into "./data/raw/". |
| The structure of "./data/raw/" should be like: |
| ``` |
| * ./data/raw/ |
| * pickup |
| * pickup_sh.csv |
| * ... |
| ``` |
|
|
| LaDe-P contains files, with each representing the data from a specific city, the detail of each city can be find in the following table. |
|
|
|
|
| | City | Description | |
| |------------|----------------------------------------------------------------------------------------------| |
| | Shanghai | One of the most prosperous cities in China, with a large number of orders per day. | |
| | Hangzhou | A big city with well-developed online e-commerce and a large number of orders per day. | |
| | Chongqing | A big city with complicated road conditions in China, with a large number of orders. | |
| | Jilin | A middle-size city in China, with a small number of orders each day. | |
| | Yantai | A small city in China, with a small number of orders every day. | |
|
|
|
|
| # 3. Description |
| Below is the detailed field of each LaDe-P. |
|
|
| | Data field | Description | Unit/format | |
| |----------------------------|----------------------------------------------|--------------| |
| | **Package information** | | | |
| | package_id | Unique identifier of each package | Id | |
| | time_window_start | Start of the required time window | Time | |
| | time_window_end | End of the required time window | Time | |
| | **Stop information** | | | |
| | lng/lat | Coordinates of each stop | Float | |
| | city | City | String | |
| | region_id | Id of the Region | String | |
| | aoi_id | Id of the AOI (Area of Interest) | Id | |
| | aoi_type | Type of the AOI | Categorical | |
| | **Courier Information** | | | |
| | courier_id | Id of the courier | Id | |
| | **Task-event Information** | | | |
| | accept_time | The time when the courier accepts the task | Time | |
| | accept_gps_time | The time of the GPS point closest to accept time | Time | |
| | accept_gps_lng/lat | Coordinates when the courier accepts the task | Float | |
| | pickup_time | The time when the courier picks up the task | Time | |
| | pickup_gps_time | The time of the GPS point closest to pickup_time | Time | |
| | pickup_gps_lng/lat | Coordinates when the courier picks up the task | Float | |
| | **Context information** | | | |
| | ds | The date of the package pickup | Date | |
|
|
|
|
| # 4. Leaderboard |
| Blow shows the performance of different methods in Shanghai. |
| ## 4.1 Route Prediction |
|
|
| Experimental results of route prediction. We use bold and underlined fonts to denote the best and runner-up model, respectively. |
|
|
| | Method | HR@3 | KRC | LSD | ED | |
| |--------------|--------------|--------------|-------------|-------------| |
| | TimeGreedy | 57.65 | 31.81 | 5.54 | 2.15 | |
| | DistanceGreedy | 60.77 | 39.81 | 5.54 | 2.15 | |
| | OR-Tools | 66.21 | 47.60 | 4.40 | 1.81 | |
| | LightGBM | 73.76 | 55.71 | 3.01 | 1.84 | |
| | FDNET | 73.27 ± 0.47 | 53.80 ± 0.58 | 3.30 ± 0.04 | 1.84 ± 0.01 | |
| | DeepRoute | 74.68 ± 0.07 | 56.60 ± 0.16 | 2.98 ± 0.01 | 1.79 ± 0.01 | |
| | Graph2Route | 74.84 ± 0.15 | 56.99 ± 0.52 | 2.86 ± 0.02 | 1.77 ± 0.01 | |
|
|
|
|
| ## 4.2 Estimated Time of Arrival Prediction |
|
|
| | Method | MAE | RMSE | ACC@30 | |
| | ------ |--------------|--------------|-------------| |
| | LightGBM | 30.99 | 35.04 | 0.59 | |
| | SPEED | 23.75 | 27.86 | 0.73 | |
| | KNN | 36.00 | 31.89 | 0.58 | |
| | MLP | 21.54 ± 2.20 | 25.05 ± 2.46 | 0.79 ± 0.04 | |
| | FDNET | 18.47 ± 0.25 | 21.44 ± 0.28 | 0.84 ± 0.01 | |
|
|
|
|
| ## 4.3 Spatio-temporal Graph Forecasting |
|
|
|
|
| | Method | MAE | RMSE | |
| |-------|-------------|-------------| |
| | HA | 4.63 | 9.91 | |
| | DCRNN | 3.69 ± 0.09 | 7.08 ± 0.12 | |
| | STGCN | 3.04 ± 0.02 | 6.42 ± 0.05 | |
| | GWNET | 3.16 ± 0.06 | 6.56 ± 0.11 | |
| | ASTGCN | 3.12 ± 0.06 | 6.48 ± 0.14 | |
| | MTGNN | 3.13 ± 0.04 | 6.51 ± 0.13 | |
| | AGCRN | 3.93 ± 0.03 | 7.99 ± 0.08 | |
| | STGNCDE | 3.74 ± 0.15 | 7.27 ± 0.16 | |
|
|
|
|
|
|
| # 5. Citation |
| To cite this repository: |
|
|
| ```shell |
| @software{pytorchgithub, |
| author = {xx}, |
| title = {xx}, |
| url = {xx}, |
| version = {0.6.x}, |
| year = {2021}, |
| } |
| ``` |
